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基础的人工智能模型和现代医疗实践

Alpay Medetalibeyoglu1, Yury S Velichko2, Eric M Hart2

  • 1Machine and Hybrid Intelligence Lab, Department of Radiology, Northwestern University, Chicago, IL 60611, United States.

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基金会的人工智能 (AI) 模型在医学上充满希望,但仔细考虑数据偏差,可解释性和资源限制对于安全有效的临床整合至关重要.

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科学领域:

  • 医疗信息学 医疗信息学
  • 人工智能在医学中的应用
  • 医疗保健技术 技术 医疗保健 技术

背景情况:

  • 医学实践的演变,从希波克拉底到现代的人工智能,共享对全面,个性化的患者护理的承诺.
  • 基金会的人工智能 (AI) 模型正在引起兴奋,特别是在医学成像中,因为它们具有数据集成和个性化治疗的潜力.

研究的目的:

  • 批判性地评估医学基础AI模型的当前状态和未来潜力,特别关注医学成像.
  • 倡导采用这些技术的衡量方法,强调在广泛临床应用之前需要解决固有的挑战.

主要方法:

  • 这篇意见稿分析了医疗实践演变与基础AI原则之间的历史平行.
  • 它确定并讨论了阻碍AI在医学成像采用的四个主要局限性:数据偏差和通用性,模型可解释性,数据稀缺性和多样性以及计算资源需求.

主要成果:

  • 医学成像中基础AI模型的广泛采用需要一种批判性和谨慎的方法.
  • 解决数据偏差,可解释性,数据多样性和基础设施等局限性至关重要,以释放AI在医疗保健中的真正潜力.

结论:

  • 严格的研究和强大的方法的文化是必要的,以确保可靠和有影响力的AI模型的医学发展.
  • 优先解决核心挑战将使通过人工智能对医疗保健进行负责任和有效的革命.